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Pirated software is an attractive choice for cybercriminals seeking to spread malicious software, known as malware. This paper attempts to quantify the occurrence of malware concealed within pirated software. We collected samples of pirated software from various sources from Southeast Asian countries, including hard disk drives, optical discs purchased in eight different countries, and online platforms using peer-to-peer services. Our dataset comprises a total of 750 pirated software samples. To analyze these samples, we employed seven distinct antivirus (AV) engines. The malware identified by the AV engines was classified into four categories: adware, Trojans, viruses, and a miscellaneous category termed others. Our findings reveal that adware and Trojans are the most prevalent types of malware, with average infection rates of 34% and 35%, respectively, among our pirated software samples. Notably, our evaluation of AV detection performance highlights variations in sensitivity, ranging from a high of 132% to a low of 30% across all AV engines. Furthermore, upon installing pirated software, the most adversely affected operating system settings are the firewall and user account control configurations. Given the potential for malware to steal information or create malicious backdoors, its high prevalence within pirated software poses a substantial security risk to end users.

We study how private intellectual property rights protection affects equilibrium prices in a duopoly competition between firms that offer a product variety of distinct qualities (vertical product differentiation) in a setup that is closely related to that put forward by Jain where firms offer the same qualities in equilibrium (horizontal product differentiation). Consumers may make a choice to buy a legal version, use an illegal copy (if they want to and can), or not use a product at all. Using an illegal version violates intellectual property rights protection and is thus punishable when discovered. Thus, both private and public (copyright) intellectual property rights protection are available on scene. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to investigate the effect of usage of pirated software on productivity by utilizing three distinct indicators of productivity for developing countries and an unbalanced panel data set containing the years between 2003 and 2017. Our hypothesis claims that increase in the use of pirated software enhance the productivity level in developing countries. Firstly, we implemented univariate analyses and the results of univariate analyses implied that the usage of pirated software and productivity are positively and significantly related in all of three models. Secondly, three control variables (i.e., education, investment, and openness) were employed in our three productivity models in order to check if the findings of univariate analyses remain valid and robust. After inclusion of the three control variables, the statistically significant positive association between the use of pirated software and productivity were observed in all models. Regarding to control variables, they take expected signs whenever they are statistically significant. Based on the estimated coefficients of PROD1 models, a 1% positive change in usage of pirated software leads to a rise in a growth in productivity by 0.1023% and 0.0623% in univariate and multivariate models, respectively while a 1% positive change in usage of pirated software leads to a rise in a growth in productivity by 0.1072% and 0.0684% in univariate and multivariate models of PROD2, respectively. Besides a 1% positive change in usage of pirated software induce to an increase in productivity level by 0.0881 and by 0.0601, respectively in PROD3 models. Secondly, three control variables, namely education, investment, and openness, were included in our three productivity models to test if the finding of univariate analyses keeps its validity. EDUC, INVEST, and OPEN variables are taken the anticipated positive influences and they are statistically significant at least at the level of 5%. More specifically, in estimation results for PROD1 model, one percent increase in EDUC, INVEST, and OPEN variables cause to a jump in productivity by 0.2309%, 0.1198%, and 0.7359%, respectively. Openness of an economy has the most explanatory power among all independent variables. In addition to PROD1 model, in estimation results for PROD2 model, one percent increase in EDUC, INVEST, and OPEN variables cause to a jump in productivity by 0.2482%, 0.0893%, and 0.7211%, respectively. Besides a 1% positive change in EDUC, INVEST, and OPEN variables induce to an increase in productivity level by 0.3345%, 0.0341%, and 0.2238%, respectively in PROD3 models. [ABSTRACT FROM AUTHOR]

Recent developments in computer networks and Internet of Things (IoT) have enabled easy access to data. But the government and business sectors face several difficulties in resolving cybersecurity network issues, like novel attacks, hackers, internet criminals, and so on. Presently, malware attacks and software piracy pose serious risks in compromising the security of IoT. They can steal confidential data which results in financial and reputational losses. The advent of machine learning (ML) and deep learning (DL) models has been employed to accomplish security in the IoT cloud environment. This article presents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection (EAGTODL-CTD) in IoT Cloud Networks. The presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud environment. The proposed EAGTODL-CTD model mainly focuses on the conversion of input binary files to color images, where the malware can be detected using an image classification problem. The EAGTODL-CTD model pre-processes the input data to transform to a compatible format. For threat detection and classification, cascaded gated recurrent unit (CGRU) model is exploited to determine class labels. Finally, EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters, showing the novelty of our work. The performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and benign. The experimental values reported the supremacy of the EAGTODL- CTD model with increased accuracy of 99.47%. [ABSTRACT FROM AUTHOR]

Applications for the Internet of Things (IoT) have been employed in a broad range of industries, including smart homes, healthcare, smart energy, and Industrial 4.0. IoT offers many advantages, such as ease and efficiency, but it also adds a lot of new risks. The problem is often made worse by the potential number of linked IoT devices and the ad hoc nature of such networks. IoT management now faces substantial difficulties related to safety and confidentiality. Current research has shown that deep learning algorithms are quite effective and have numerous benefits over previous approaches for doing security assessments of IoT devices. An integrated deep learning approach is created in this work to identify files containing malware and pirated software through the Internet of Things network The Hybrid Dual-Channel Convolution Neural Network (DCCNN) with Spider Monkey Optimization (SMO) is called DCCNN-SMO advocated leveraging stolen reference code to detect software piracy. For the purpose of examining software piracy, the dataset was gathered via Google Code Jam (GCJ). To research the suggested strategy, collected 100 input data for internet users from GCJ. In addition, the DCCNN-SMO is utilized to visualize colored images to identify harmful intrusions in IoT networks. From the Leopard Mobile database, malware samples were taken for testing. The actual results demonstrate that the recommended method for calculating the cyber security risks in IoT performs categorization more over current approaches. The proposed DCCNN-SMO + SVM approach provides better resultant value of 98.55% whereas the other existing approaches like GIST + SVM is 86.1%, CLGM + SVM is 92.06% and DNN + SVM is 97.46%, LBP + SVM is 78.05%.

Measures of ethical behaviours in research may be subject to social desirability bias (SDB). Most behavioural aspects of software piracy research are a subset of ethical research. Few studies of software piracy have explicitly addressed the issue of SDB. In this article, the extent of SDB that may be present in software piracy is empirically assessed. This was done by comparing SDB in software piracy under three conditions using the 40-item Balanced Inventory of Desirable Responding (BIDR) scale. First, software piracy behaviour was examined in an experimental setting with actual money at stake. This allowed subject behaviour to be observed rather than inferred from self-report responses. Second, a survey was conducted to test the extent of software piracy based on subject self-report responses. Third, the extent of software piracy was also assessed based on peer-report responses (i.e.: the subject reported whether their peers would pirate). A comparison of the responses in the three conditions allows us to draw inferences about SDB in software piracy research. Self-report surveys exhibited high levels of SDB. The behaviour in the experimental condition, and responses in the peer-report survey method, did not exhibit SDB. [ABSTRACT FROM AUTHOR]

While the intention and participation in media and software piracy have been widely investigated, little is known concerning the determinants associated with electronic book piracy. With the adoption of e-book reader devices, Internet piracy remains a global concern that impacts multiple stakeholders, including authors, publishers, and libraries. The current study aims to examine the factors associated with electronic book piracy among college students. Results indicate that peer association and perceived risk are significantly linked to one's participation in e-book piracy. However, techniques of neutralization are not statistically significant. Libraries and publishers must educate students and raise awareness about the potential outcomes and risks related to e-book piracy. [ABSTRACT FROM AUTHOR]

Piracy is a significant source of concern facing software developers, music labels, and movie production companies, to name a few. Digital goods producers and government entities argue that there are victims of piracy, whereas pirates may perceive their actions to be victimless. Regarding implications of our research, we extend the theory of planned behavior (TPB) by theorizing that attitudes, subjective norms, and perceived behavioral control could influence perceptions of moral obligations as a consequence of the desire to rationalize unethical behavior. Unlike prior literature, we manipulate the rationalization of moral obligations due to the victimless view toward piracy and show how moral obligations become important determinants of piracy behavior. Accordingly, our demonstrated malleability of morals may be an important path through which individuals are able to continue past behaviors. We also conduct a second study to identify the effect of implementing an educational message from a fictitious software company to exogenously nudge the pirate and influence the impact of perceived moral obligations on intentions to pirate. Our results show that the introduction of an exogenous educational message is an effective piracy mitigation strategy. [ABSTRACT FROM AUTHOR]

This paper explores the dynamic long‐term relationship between software protection and software piracy rates across 108 countries over the period of 20 years (1994–2015), with the help of the ARDL model. The analysis employs a new way of utilising the membership duration of Trade related Intellectual Property Rights agreement and World Intellectual Property Organisation Copyright Treaty agreements of different countries in a panel framework, apart from constructing an index of copyright software protection by focusing on enforcement provisions. The paper overcomes various shortcomings of earlier literatures by thoughtful inclusion of independent variables and methodological techniques. Results show that both copyright software protection and income share a negative long‐term relationship with software piracy. Even the enforcement provisions are important in mitigating software piracy. In addition, the model finds a negative relationship between demand for intellectual property protection and software piracy rates. [ABSTRACT FROM AUTHOR]

This study analyzes the effect of usage of pirated software on poverty by using six prominent poverty indicators for the samples of developing and Latin America countries. The data utilized in models is unbalanced and employ the period between 2003 and 2017. Our hypothesis asserts that increases in usage of pirated software diminish poverty in developing and Latin America countries. Firstly, univariate analyses are conducted, and the results of univariate analyses are demonstrated that there is a statistically significant opposite relationship between usage of pirated software and poverty in all six distinct poverty models. Secondly, three control variables (i.e., unemployment, health expenditure, and human capital) were included to our six poverty models to find out if the finding of univariate analyses retains its validity. The statistically significant reverse relationship between usage of pirated software and poverty remained the same after the inclusion of the three covariates. In other words, usage of pirated software maintains its negative significant effect on poverty in all models for both developing and Latin America countries samples. In consideration of the control variables, statistically significant negative coefficients were obtained for health expenditure and human capital whereas a positive coefficient was obtained for unemployment. [ABSTRACT FROM AUTHOR]

This paper focuses on one of the topics of copyright economics: the study of software piracy and its determinants. The efficiency of OECD countries regarding the consumption of illegal software is analyzed. In this vein, efficiency is associated with the minimal consumption of software piracy according to the socioeconomic characteristics of a nation. Data Envelopment Analysis is the methodology employed, which assigns an efficiency score to the countries in order to establish a ranking of efficiency. Additionally, a relationship is established between the legal origin of the copyright law of a country and its efficiency level. The results of the efficiency analysis show that the efficient countries are Austria, Hungary, Japan, Korea, Mexico and Slovakia; this leads to the affirmation that the countries with lower levels of piracy are not always efficient. According to the legal origin of copyright law, countries with a German origin are the most efficient. Consequently, the efficiency score is not related to the level of software piracy but to the legal origin. [ABSTRACT FROM AUTHOR]

Sci-Hub is a project that uses stolen log-in credentials to access pay-walled scientific articles and journals, and then makes them available for free on its website. It has had a significant impact on medical journals. The HEC Digital Library does not cover all research areas. The Higher Education Commission (HEC) has acknowledged that Sci-Hub could fill a gap in the availability of research materials for Pakistani researchers but cautioned that the use of Sci-Hub is illegal and could have negative consequences for the scientific community. Sci-Hub has faced legal challenges in some countries but has generally been able to operate without interference. The legality of Sci-Hub is complex, with legal challenges varying by country. There are legal ways to access scientific manuscripts. The Sci-Hub project has significantly impacted research in Pakistan, but the HEC does not condone the use of Sci-Hub, and it could have negative consequences for the scientific community. [ABSTRACT FROM AUTHOR]

Why do mainstream consumers who would not typically engage in illegal behaviour routinely resort to online piracy of copyrighted software? This paper provides answers to this research query by applying routine activity theory and the theory of reasoned action. The paper’s study analyses consumers’ role as possible offenders that can have the opportunity to engage in online software piracy as part of their routine online activities. Although it is problematic to measure the exact magnitude of the negative impact on the US economy, as stated by the Government Accountability Office it is sizeable. After analysing the conceptual model using a US national consumer sample of over 700 consumers, results show the influence of proximity to motivated offenders, target suitability, and capable guardianship on consumers’ attitudes and perceived subjective norms towards online software piracy, as well as their intentions to engage in this illegal behaviour on the Internet. By integrating routine activity theory, a criminology theory with the theory of reasoned action from psychology and analysing a widespread online software piracy phenomenon, several academic and practical contributions are made. [ABSTRACT FROM AUTHOR]